Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models

Area of Science:

• Cardiovascular Physiology
• Medical Imaging & Diagnostics
• Computational Biology

Background:

• Distinguishing pre-eclampsia from hypertension in pregnancy is challenging using traditional pressure measurements.
• Pregnancy significantly alters the maternal cardiovascular system, necessitating advanced analytical tools.
• Existing biomarkers for pre-eclampsia lack sufficient accuracy for reliable classification.

Purpose of the Study:

• To develop and validate novel non-invasive biomarkers for accurate pre-eclampsia classification.
• To utilize computational modeling for detailed analysis of cardiovascular changes during pregnancy.
• To improve diagnostic capabilities for early and late onset pre-eclampsia.

Main Methods:

• Integrated six non-invasive cardiovascular signals to create personalized cardiovascular computational models.
• Generated predictions of blood pressure and flow waveforms in the utero-ovarian vascular system.
• Calculated novel pressure pulsatility index (PPI) and resistance index (RI) in arcuate and radial/spiral arteries.

Main Results:

• Computational models successfully predicted blood pressure and flow in utero-ovarian vessels.
• The novel PPI and RI in smaller arteries significantly differentiated pre-eclamptic from non-pre-eclamptic women (p < .001).
• These new biomarkers outperformed traditional PI and RI derived from uterine artery Doppler measurements.

Conclusions:

• Two novel, high-performing biomarkers (PPI and RI) for pre-eclampsia classification have been identified.
• These biomarkers are based on predicted blood velocity and pressure in smaller placental vessels.
• The findings offer a promising non-invasive approach for improved pre-eclampsia diagnosis.